Segmentation and yield count of an arecanut bunch using deep learning techniques - Arekattedoddi Chikkalingaiah, Anitha; Dhanesha, RudraNaik; Chikkathore Palya Laxmana, Shrinivasa Naika; Neelegowda, Krishna Alabujanahalli; Mangala Puttaswamy, Anirudh; Ayengar, Pushkar (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
Arecanut is one of Southeast Asia’s most significant commercial crops. This work aims at helping arecanut farmers get an estimate of the yield of their orchards. This paper presents deep-learning-based methods for segmenting arecanut bunch from the images and yield estimation. Segmentation is a fundamental task in any vision-based system for crop growth monitoring and is done using U-Net squared model. The yield of the crop is estimated using Yolov4. Experiments were done to measure the performance and compared with benchmark segmentation and yield estimation with other commodities, as there were no benchmarks for the arecanut. U-Net squared model has achieved a training accuracy of 88% and validation accuracy of 85%. Yolo shows excellent performance of 94.7% accuracy for segmented images, which is very good compared to similar crops.